Word Sense Induction is a task of automatically finding word senses from large scale texts. It is generally considered as an unsupervised clustering problem. This paper introduces a hypergraph model in which nodes represent instances of contexts where a target word occurs and hyperedges represent higher-order semantic relatedness among instances. A lexical chain based method is used for discove...

Recent advances in word sense induction rely on clustering related words. In this paper, instead of using a clustering algorithm, we suggest to perform a Singular Value Decomposition (SVD) which can be guaranteed to always find a global optimum. However, in order to apply this method to the problem of word sense induction, a semantic interpretation of the dimensions computed by the SVD is requi...

To solve the problems in traditional automatic Chinese summarization, a new method based on the word concept and clustering is presented in this paper. Different from the normal statistical method, concept is used as feature instead of word. Also, instead of word frequency statistics, word concept frequency statistics (WCFS) is used in our approach. For each paragraph, a conceptual vector space...

With the aim of constructing a set of prosodic rules enabling to generate high-quality synthetic speech of Chinese, tone concatenation features were investigated for Chinese words. A statistical model is developed for Chinese word pitch contours based on fuzzy clustering and analysis method. The clustering results shows that word contours are not only depending on the different combination of t...

Our team participated in the Chemical Compound and Drug Name Recognition task of BioCreative IV. We used a mixed conditional random fields with word clustering to fulfillment this task. For one hand, we generate the word feature by word clustering and train the corpus with word feature to get one model. On the other hand, the training corpus is transformed to a new one in the reversed order of ...

background: finding the right word is a necessity in communication, and its evaluation has always been a challenging clinical issue, suggesting the need for valid and reliable measurements. the homophone meaning generation test (hmgt) can measure the ability to switch between verbal concepts, which is required in word retrieval. the purpose of this study was to adapt and validate the persian ve...

This paper follows a word-document co-clustering model independently introduced in 2001 by several authors such as I.S. Dhillon, H. Zha and C. Ding. This model consists in creating a bipartite graph based on word frequencies in documents, and whose vertices are both documents and words. The created bipartite graph is then partitioned in a way that minimizes the normalized cut objective function...

This paper presents an automatic method for reducing the part-of-speech tagset to be considered by a word prediction system in Portuguese. The method is based on a similarity measure applied to a association matrix, generated by employing a odds ratio association measure in the bigrams of parts-of-speech (bipos) probability distribution in a corpus. The results reported in this paper show that ...

We introduce tiered clustering, a mixture model capable of accounting for varying degrees of shared (context-independent) feature structure, and demonstrate its applicability to inferring distributed representations of word meaning. Common tasks in lexical semantics such as word relatedness or selectional preference can benefit from modeling such structure: Polysemous word usage is often govern...

A technique is presented that determines equivalences between word images in a passage of text. A clustering procedure is applied to group visually similar words. Initial hypotheses for the identities of words are then generated by matching the word groups to language statistics that predict the frequency at which certain words will occur. This is followed by a recognition step that assigns ide...